In a development that will help scientists
better determine how many large proteins work, Berkeley Lab's Paul Adams
and collaborators used the latest advances in computational analysis to
study how a complex biological machine refolds proteins, a process critical
to cell survival.

A few frames of crystallographic data can reveal how large portions of
a molecular machine, in this case the chaperonin GroEL, move. (Numbers
indicate labeled peptides, used as references to track the motion of the
protein's individual components.)

They took advantage of the fact that the protein, called a chaperonin,
encapsulates unfolded proteins by moving large portions of its structure
in unison, like a hand clenching a marble. This means that only a few
frames of crystallographic images, each revealing the protein at a different
stage of its motion, are needed to picture the entire process. And because
many large proteins are believed to behave this way, their work also means
that the mountain of crystallographic data collected over the past ten
years  much of which only portray large-scale changes in proteins
 can help determine how many proteins twist and turn.

"We can get more out of crystallographic experiments than we thought
we could," says Adams, a staff scientist with Berkeley Lab's Physical
Biosciences Division and the deputy principal investigator of the Berkeley
Structural Genomics Center. "Although we often can't model the behavior
of a protein atom by atom, it is possible to visualize how large portions
of a protein's atoms move together."

As of October, 2004, there were more than 27,000 structures in the Protein
Data Bank, a worldwide repository of three-dimensional structures of large
proteins and nucleic acids. Not all of these proteins lend themselves
to this broad-brush analysis, because not all of them exhibit what's called
rigid body movement, in which discrete groups of their atoms move as a
single unit. But for those that do, Adams's technique could offer an efficient
way to understand how they move.

His team, which included Charu Chaudhry and Arthur Horwich of Yale University
and Axel Brunger of Stanford University, developed the method using a
chaperonin found in Escherichia coli called GroEL. Like similar
molecules found in the cells of all organisms, GroEL repairs proteins
that unfold due to adverse conditions such as heat stress. If too many
proteins unfold, they cling together and cause cell damage, even death.
To keep cells healthy, GroEL somehow surrounds a poorly folded protein,
isolates it with a lid, then quickly stretches the protein before letting
go, helping it move back to its correct shape.

To visualize how GroEL conducts this rescue work, the team used crystallographic
data representing the protein in four different configurations: with its
lid off, with its lid on, bound to the energy-providing enzyme adenosine
triphosphate, and immediately after it attempts to refold a protein. They
then ran this data through newly developed software that calculates how
groups of atoms within the protein rotate and translate around fixed points.
This analysis revealed the direction in which each of the protein's parts,
or rigid bodies, was moving at the time the data sets were taken. In this
way, they were able to model GroEL's movement using only about 20 parameters
per rigid body, as opposed to the thousands of parameters needed to model
a protein's atom-by-atom movement.

"We can see the protein's large conformational changes through these
snapshots," says Adams. "The original crystal images are hugely
informative, but we want to add to these static pictures information about
how large groups of atoms move in the molecule."

Their success marks one of the first times this method has been used
to elucidate how parts of a large molecular machine move. As such, it
offers a way to experimentally validate theoretically and computationally
derived models of molecular motion. Adams believes the technique could
also soon help reveal the dynamics of other molecular machines that possibly
change shape via large, sweeping motions, such as the proteins that make
up muscles or molecules that help DNA replicate. And to make the process
more efficient, his group is developing an automated way to determine
which atoms team up and behave like rigid bodies.

"We could take a structure, figure out its rigid bodies, and then
perform the analysis with the experimental data," says Adams. "Our
overall goal is to take crystallographic data and extract information
about large motions in big systems to better understand how they work."